Multidimensional characterization of mobile apps for hearing health care by using the ALFA4Hearing (At-a-glance Labelling for Features of Apps for Hearing health care) model combined with data visualization methods

Introduction. Hearing loss is one of the most prominent health burdens, with over 360 million sufferers worldwide. Medical apps can become key drivers for pervasive, effective hearing health care (HHC) for very large population with hearing problems, however there still lack of methods for informed, aware adoption of apps. The use of specific models for app assessment is crucial to identify the relevant attributes and to highlight the emerging needs in the HHC field. Moreover, due to the usually large set of features needed to characterize apps, it is also important to devise informative methods for data analysis so to extract focused and relevant information in a given study sample.
Method. We here develop a novel approach for the characterization of mobile apps for HHC that combines the ALFA4Hearing model (At-a-glance Labelling for Features of Apps for Hearing health care) with data visualization techniques. The ALFA4Hearing model is a recently developed method for apps for HHC that characterizes apps against a core set of 29 features grouped into five components (Promoters, Services, Implementation, Users, and Descriptive Information). The model is used here to provide descriptive pictures of a sample of 120 apps (iOS and Android) covering the whole spectrum of services for HHC. Data visualization techniques are used here to analyze the relationships between the model components and features in our sample of apps as well as in specific subsets. We analyze data by using several data visualization techniques, with different weighted graphs algorithms and network layouts, with varying number of layers and nodes.
Results. We found that, among the several data visualization approaches here tested, three methods showed greater potential. (i) A two-dimensional cluster graph was found to be helpful to describe the relevance of the different components in the model and the distribution of features within each component. (ii) A three-dimensional, three-layer layout was found to be explanatory about the multidimensional relationships between the apps (layer 1), the features (layer 2) and the model components (layer 3), and made it easy to extract information about specific apps, subsets of apps, or clusters of features. (iii) A two-dimensional unconstrained graph was able to highlight effectively the relationships among features within- as well as between- domains in any given app sample.
Discussion and Conclusions. In this study, we were able to identify three graph layouts in order to effectively highlight the relationships among the app features. The relevance of these features and the role of the different model components in any sample of apps could be described by a clear representation. Our combined approach provide a promising tool able to represent a large amount of information from multiple perspectives so to study the current trends in the field. Moreover, this approach could be of great value to identify emerging research questions and potential opportunities for developers, stakeholders, or clinicians and drive their directions for research, professional training, clinical use of apps, as well as technical developments.